Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Meiburger, Kristen M.; Salvi, Massimo; Rotunno, Giulia; Drexler, Wolfgang; Liu, Mengyang

doi:10.3390/app11209734

Open AccessReview

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

by

Kristen M. Meiburger

^1,*

,

Massimo Salvi

¹

,

Giulia Rotunno

²,

Wolfgang Drexler

² and

Mengyang Liu

²

¹

Biolab, Polito^BIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy

²

Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(20), 9734; https://doi.org/10.3390/app11209734

Submission received: 16 September 2021 / Revised: 9 October 2021 / Accepted: 13 October 2021 / Published: 18 October 2021

(This article belongs to the Special Issue Functional Optical Coherence Tomography (OCT) for Biomedical Application)

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Abstract

Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.

Keywords: optical coherence tomography angiography; segmentation; classification; review; handbook

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MDPI and ACS Style

Meiburger, K.M.; Salvi, M.; Rotunno, G.; Drexler, W.; Liu, M. Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook. Appl. Sci. 2021, 11, 9734. https://doi.org/10.3390/app11209734

AMA Style

Meiburger KM, Salvi M, Rotunno G, Drexler W, Liu M. Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook. Applied Sciences. 2021; 11(20):9734. https://doi.org/10.3390/app11209734

Chicago/Turabian Style

Meiburger, Kristen M., Massimo Salvi, Giulia Rotunno, Wolfgang Drexler, and Mengyang Liu. 2021. "Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook" Applied Sciences 11, no. 20: 9734. https://doi.org/10.3390/app11209734

APA Style

Meiburger, K. M., Salvi, M., Rotunno, G., Drexler, W., & Liu, M. (2021). Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook. Applied Sciences, 11(20), 9734. https://doi.org/10.3390/app11209734

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

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